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09 Sep 2019 | Teaching Materials | Contributor(s):
By Claudia Neuhauser
University of Houston
A Numbers Count statistics diagram
REMNet Tutorial, R Part 5: Normalizing Microbiome Data in R 5.2.19
28 Aug 2019 | Teaching Materials | Contributor(s):
By Jessica Joyner
CUNY Brooklyn College
Video on normalizing microbiome data from the Research Experiences in Microbiomes Network
Hwayeon Ryu - Bio-Stats learning community
01 Aug 2019 |
Posted by Carrie Diaz Eaton
out of 5 stars
24 Jul 2019 | | Contributor(s):: Drew LaMar
Stripped down version of Radiant on QUBES
03 Jun 2019 | Teaching Materials | Contributor(s):
By Lisa Dierker
E-book in .pdf format and customizable .iba format
MS in Data Science at University of Delaware
23 Jan 2019 |
Posted by Alycia Crall
Teaching RNAseq at Undergraduate Institutions: A tutorial and R package from the Genome Consortium for Active Teaching
04 Jan 2019 | Teaching Materials | Contributor(s):
By Mark Phillip Peterson1, Jacob T Malloy2, Vincent P Buonaccorsi2, James H Marden3
1. Viterbo University 2. Juniata College 3. Pennsylvania State University
This lesson plan was created to teach RNAseq analysis as a part of GCAT-SEEK network. It is provided here, both in finished form and with the modifiable source code, to allow flexible adaptation to...
Kate S Boersma
05 Nov 2018 | Teaching Materials
DataCamp: The Easiest Way to Learn Data Science Online
50 Years of Data Science
30 Oct 2018 | Teaching Materials | Contributor(s):
By David Donoho
This paper reviews some ingredients of the current “Data Science moment”, including recentcommentary about data science in the popular media, and about how/whether Data Science isreally different...
Java Applets for Power and Sample Size
By Russ Lenth
Statistics and Actuarial Science Department, University of Iowa
This is a Java application that allows you to do power and sample-size calculations for a number of standard statistical models.
Statistics for Biologists
By Magdalena Skipper1, Ritu Dhand1, Helen Pearson1, Francesca Cesari1
There is no disputing the importance of statistical analysis in biological research, but too often it is considered only after an experiment is completed, when it may be too late.